Embodiments of the present invention generally relate to the field of databases, and more specifically, to methods and systems for storing and querying data.
Storage and query for data, especially mass data, are of significant importance for database. In recent years, with the development of relevant fields such as science and computation, this issue becomes particularly prominent. As an example, various kinds of satellites may shoot images of one or more areas of the earth or other target object, for various purposes such as scientific research, remote sensing survey, and weather report. Satellite image data has considerable data amount and may be continuously captured and stored within a period of time. Moreover, such data is generally associated with different dimensions. For example, the data of a satellite image at least may be associated with time dimension and spatial dimension. The time dimension may indicate the time or period when the image is captured, while the spatial dimension may indicate the scope of the geographical area included in the image, e.g., limited by latitude coordinate and longitude coordinate.
Multi-dimensional data such as satellite image data poses a great challenge to storage and query of data. A larger data amount results in that the data always has to be distributed in a great number of files, which increases maintenance overheads. Moreover, query of such data is always a multi-dimensional query. In other words, a data query request includes a query condition involving multiple different dimensions, which increases the difficulty and complexity of data query. Moreover, such query process always requires a considerable number of input/output (I/O) operations, which inevitably dampens query efficiency and prolongs response delay.
A traditional solution for data storage and query cannot perform storage and query effectively. For example, a known file system cannot support an effective multi-dimensional query and cannot guarantee I/O efficiency. A relational database based on a “relation-entity” model cannot effectively store multi-dimensional data. Although a database specific for mass data (e.g., scientific array data) has been proposed, such database can only solve the storage issue of data. Multi-dimensional query of multi-dimensional data is still limited by lower efficiency.
In view of the above, there is a need for a solution that can store and query multi-dimensional data more effectively.